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Survival Estimates of White-tailed Deer Fawns at Fort Rucker, Alabama
by
Angela Marie Jackson
A thesis submitted to the Graduate Faculty of
Auburn University
in partial fulfillment of the
requirements for the Degree of
Master of Science
Auburn, Alabama
August 6, 2011
Keywords: White-tailed deer, fawn survival, coyote, predator-prey theory
Copyright 2011 by Angela Marie Jackson
Approved by
Stephen S. Ditchkoff, Chair, Professor of Forestry and Wildlife Sciences
Mark D. Smith, Assistant Professor of Forestry and Wildlife Sciences
Todd D. Steury, Assistant Professor of Forestry and Wildlife Sciences
ii
Abstract
Decreases in recruitment of white-tailed deer (Odocoileus virginianus) fawns
have been noted at several locations across the Southeast. Understanding the reason for
these decreases is important for management of deer populations. We monitored fawns
from birth until 6 months to examine age- and cause-specific rates of mortality, at Fort
Rucker, Alabama, a location that has experienced substantial decreases in fawn
recruitment, deer population density, and hunter success. This study, like other recent
studies in the Southeast, has found that low fawn recruitment seems to be driven by
greater levels of coyote (Canis latrans) predation than originally believed. Coyotes are a
recent addition to the predator community of the Southeast, but how their addition will
ultimately affect deer populations remains unknown. Predator-prey theory predicts a
variety of future scenarios concerning predation rates, deer density, and responses to
alternative management strategies. We describe these alternative theories in regard to the
current state of knowledge.
iii
Acknowledgments
I would like to thank Dr. Steve Ditchkoff for selecting me for this project and for
his support and encouragement. I also thank Drs. Todd Steury and Mark Smith for their
guidance and assistance. This project was funded by the Department of Defense, U.S.
Army and funds were obtained by the Natural Resources Branch at Fort Rucker,
Alabama. I am appreciative of the Natural Resources Branch for all of their support.
My technician Amy Lynch was a crucial contributor to my field work, and I am
greatly appreciative of the hard work and good attitude that she put forth. Charles Mayo,
Daniel Spillers, J.B. Bruner, Kerwin Gulledge, and Brian Mooney provided the
motivation, ingenuity, long hours, and brute force required to complete data collection. I
thank them for their assistance and effort to make this a success. I thank Chad Newbolt
for assistance with initial set up at the field site as well as continued support and
assistance. Additionally I would like to thank the volunteers who helped with field work
throughout the project, particularly Kyle Waters, Wesley Holland, Michelle Tacconelli,
Jesse Boulerice, Allie Hunter, and Matthew Zirbel. Alabama DCNR provided man hour
support during the 2009 trapping season and this is greatly appreciated.
I am always grateful to my loving mother for her support and encouragement.
Not only did she provide support from home but assisted with field work! My boyfriend,
Wesley Holland, has shown great patience and understanding while helping and
supporting me throughout the project. They have kept me both balanced and motivated.
iv
Table of Contents
Abstract ............................................................................................................................... ii
Acknowledgments.............................................................................................................. iii
List of Figures .................................................................................................................... vi
List of Tables .................................................................................................................... vii
Chapter I: Literature Review ...............................................................................................1
Fawn Survival ..........................................................................................................1
Overall mortality rates .................................................................................1
Temporal patterns ........................................................................................1
Differences in survival of the sexes .............................................................3
Predation ......................................................................................................3
Other causes of mortality .............................................................................5
Doe Survival ............................................................................................................6
Overall survival ............................................................................................6
Hunting mortality .........................................................................................7
Disease .........................................................................................................7
Vehicle collisions .........................................................................................8
Predation ......................................................................................................8
Coyote Food Habits .................................................................................................9
Bobcat Food Habits................................................................................................11
v
Predator-Prey Theory .............................................................................................12
Basic concepts ............................................................................................12
Limitation ...................................................................................................13
Regulation ..................................................................................................15
Literature Cited ......................................................................................................19
Chapter II: Survival Estimates of White-tailed Deer Fawns at Fort Rucker, Alabama .....30
Abstract ..................................................................................................................30
Introduction ............................................................................................................31
Study Area .............................................................................................................34
Methods..................................................................................................................35
Doe capture and handling ..........................................................................35
Fawn capture and handling ........................................................................36
Analysis......................................................................................................38
Coyote density ...........................................................................................39
Results ....................................................................................................................39
Discussion ..............................................................................................................41
Literature Cited ......................................................................................................49
Figures....................................................................................................................57
Appendix I ........................................................................................................................59
Appendix II ........................................................................................................................61
vi
List of Figures
Chapter II
Figure 1. Survival of white-tailed deer fawns from birth to 180 days of age at Fort
Rucker, AL during 2009 and 2010. .......................................................................57
Figure 2. Annual survival of female white-tailed deer at Fort Rucker, AL. These data
were determined based on survival from capture week in either 2009 or 2010 until
the end of the study in February 2011. ..................................................................58
Appendix II
Figure 1. The interaction between the population growth curve and the Type III
functional response resulting in two stable equilibria, A and C, and one unstable
equilibrium, B, also known as the multi-stable state or predator pit theory. .........61
Figure 2. The interaction between the population growth curve and the Type III
functional response resulting in one stable equilibrium, A, also known as the
regulation theory. ...................................................................................................62
vii
List of Tables
Appendix I
Table 1. Doe data ...............................................................................................................59
Table 2. Fawn data .............................................................................................................60
1
Chapter I: Literature Review
FAWN SURVIVAL
Overall mortality rates
In the late 20th
century Linnell et al. (1995) reviewed studies of neonatal mortality
in ungulates, including 19 studies on white-tailed deer (Odocoileus virginianus). Across
these 19 studies the average percentage of mortality was 46% with a standard deviation
of ±28%. This large standard deviation indicates that these studies varied greatly in the
percent mortality reported, and a small selection of the reviewed studies demonstrates
this variation (71%, Cook et al. 1971; 90%, Bartush and Lewis 1981; 85%, Epstein et al.
1983; 27%, Huegel et al. 1985). Since the review published by Linnell et al. (1995),
mortality of white-tailed deer fawns continues to be examined: reports of fawn mortality
published soon after the review include Sams et al. (47%, 1996) and Long et al. (74%,
1998). Pusateri Burroughs et al. (2006), Ricca et al. (2002) and Vreeland et al. (2004)
reported mortality rates of 16%, 59%, and 49%, respectively, to approximately 6 months
of age. The most recent studies on white-tailed deer fawn survival have used VITs to
locate very young and stillborn individuals, and thus were able to calculate more accurate
mortality estimates (66.7%, Saalfeld and Ditchkoff 2007; 53%, Carstensen et al. 2009;
77%, Kilgo unpublished data).
Temporal patterns
As a hider species, white-tailed deer fawns remain still and hidden when
presented with stressful stimuli (Lent 1974). This response, known as the prone
2
response, wanes as the fawn ages, and white-tailed deer demonstrate a decrease in this
response as early as 10 days of age (Downing and McGinnes 1969). Young of hider
species also become more active as they age and may require activity to develop muscles
which allow them to outrun predators (FitzGibbon 1990). This increase in activity causes
fawns to become more visible at a time when they are unable to outrun predators and thus
become more vulnerable to predation (Litvaitis and Shaw 1980, Byers and Byers 1983,
Aanes and Andersen 1996). The time of vulnerability to predators is not constant across
white-tailed deer fawn mortality studies. Bartush and Lewis (1981) found the age at
which predation occurred to range from 3 to 78 days with an average age of 21, while
73% of canid-killed fawns were 27-47 days old in a study by Nelson and Woolf (1987).
In the extremes of variability, however, within days of being marked 14 of 20 fawns were
killed by predators in a study by Carroll and Brown (1977), while the canid-killed fawns
studied by Nelson and Woolf (1987) were all over 20 days of age. It seems as though
most fawns can elude predators by 8 weeks (Nelson and Woolf 1987, Long et al. 1998).
Approximately 90% of all mortality occurred within 30 days of birth in two studies
(Cook et al. 1971, Bartush and Lewis 1981), indicating that during this time white-tailed
deer fawns are most vulnerable to mortality.
Studies on other ungulate hider species have shown similar results in the timing of
mortality. Trainer et al. (1981) determined that the greatest mortality occurred within 45
days of age for mule deer (Odocoileus hemionus) fawns. The median age in days for
fawns killed by predators was 20, while non-predatory mortality occurred at a median age
of 13 days (Trainer et al. 1981). Another study found that mule deer fawns were most
susceptible to predation by coyotes between 45 and 105 days of age (Hamlin et al. 1984).
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This variation is similar to that found in white-tailed deer fawns. Pronghorn (Antilocapra
americana) may have an earlier window of susceptibility as studies have found them to
be most susceptible to mortality within three weeks of age (Von Gunten 1978) and
susceptible to predators between 11 and 20 days old (Barrett 1978, 1984).
Differences in the survival of the sexes
Most studies on white-tailed deer fawn mortality have found no difference in the
survival of male versus female fawns (Cook et al. 1971, Bartush and Lewis 1981, Nelson
and Woolf 1987, Decker et al. 1992, Sams et al. 1996, Long et al. 1998, Ricca et al.
2002), with similar results in mule deer (Zwank 1978, Trainer et al. 1981). However, a
study by Carstensen et al. (2009) determined that three times as many males as females
died between 5 and 12 weeks of age. This time interval is when fawns are considered to
be most susceptible to predation due to an increase in activity, and males are more active
than females (Jackson et al. 1972), potentially making them more visible to predators.
Another reason for greater rates of mortality in males may be due to increased nutritional
demands in males versus females of polygynous mammals. Male fawns have a higher
birth weight and growth rate than female fawns (Wauters et al. 1995, Birgersson and
Ekvall 1997) which places greater nutritional demands on the mother (Clutton-Brock et
al. 1981). The effect of nutritional demands on the mother and the survival of her
offspring can be seen in a study by Verme (1962) which showed decreased survival in
fawns whose mothers were fed restricted diets.
Predation
Predators can substantially increase rates of mortality, and predation has been
reported to account for 89% of mortalities (Kilgo unpublished data). A review by Linnell
4
et al. (1995) found that an average of 54% of mortality was due to predation, but high
variability in the percentage of mortality due to predation is seen across studies (88%,
Bartush and Lewis 1981; 63%, Epstein et al. 1983; 57%, Ricca et al. 2002; 17-70%,
Vreeland et al. 2004; 86%, Carstensen et al. 2009). The type and density of predators in
an area has a strong influence on rate of predation.
Potential predators on my study site, Fort Rucker, Alabama, include coyotes
(Canis latrans), domestic dogs (Canis lupus familiaris), bobcats (Lynx rufus), black bears
(Ursus americanus), red foxes (Vulpes vulpes) and grey foxes (Urocyon
cinereoargenteus). Coyotes can cause significant mortality in white-tailed deer fawns,
and it has been suggested that they have effectively filled the niche of wolves (Canis
lupus) in some areas as a fawn predator (Ballard et al. 1999). Coyotes have been found
to be a significant predator of white-tailed deer fawns (60% of predation due to coyotes,
Cook et al. 1971; 70%, Huegel et al. 1985; 66.7%, Sams et al. 1996; 80%, Long et al.
1998; 100%, Saalfeld and Ditchkoff 2007; 90%, Kilgo unpublished data). Other studies
have noted incidents of coyote predation but to a lesser extent (Mathews and Porter 1988,
Decker et al. 1992, Vreeland et al. 2004). Nelson and Woolf (1987) were unable to
distinguish between coyote and domestic dog kills and attributed 69% of mortalities to a
combination of the species. Domestic dog predation has also been noted in other studies
(Huegel et al. 1985, Decker et al. 1992, Long et al. 1998, Ricca et al. 2002). Lowry and
McArthur (1978) reported incidences of domestic dogs chasing white-tailed deer and
mule deer, with 39 reported chases resulting in 12 deaths.
In some studies bobcats have also been reported to be significant predators of
white-tailed deer fawns (46% of predation due to bobcats, Epstein et al. 1983; 75% of
5
predation by known predators due to bobcats, Ricca et al. 2002; 90% of predation due to
bobcats, Roberts 2007; 38% of predation due to bobcats, Carstensen et al. 2009).
However, bobcats are considered to be a less significant predator in most studies (Cook et
al. 1971, Decker et al. 1992, Sams et al. 1996). Bobcats, an ambush predator, may be a
less significant predator of white-tailed deer fawns, in comparison to coyotes, because
they sit and wait for prey and would be less likely to encounter hiding white-tailed deer
fawns. Other predators of white-tailed deer fawns include black bears (Mathews and
Porter 1988, Kunkel and Mech 1994, Vreeland et al. 2004, Carstensen et al. 2009), both
red and grey foxes (Epstein et al. 1983, Sams et al. 1996, Ricca et al. 2002), and
alligators (Alligator mississippiensis; Epstein et al. 1983, Roberts 2007).
Other causes of mortality
Mortality due to factors other than predation are common in most studies;
however, predation is usually the leading cause of mortality. Other factors leading to
mortality include disease, emaciation/abandonment, drowning, accidents, and poaching.
Emaciation and starvation are both caused by malnutrition of the fawn. This malnutrition
may be due to malnutrition in the doe (Verme 1962), leading to compromised lactation,
or abandonment by the doe. Several studies have reported deaths due to malnutrition
(Cook et al. 1971, Bartush and Lewis 1981, Nelson and Woolf 1987, Sams et al. 1996,
Ricca et al. 2002, Vreeland et al. 2004, Pusateri Burroughs et al. 2006, Saalfeld and
Ditchkoff 2007). Disease has been reported by Cook et al. (abscess, diarrhea,
salmonellosis, 1971), Decker et al. (1992), Huegel et al. (acute necrotic hepatitis, 1985),
Nelson and Woolf (1987), Pusateri Burroughs et al. (pneumonia and bacterial infection,
2006) and Ricca et al. (2002). Disease may also be affected by nutrition because the
6
immune system can be compromised in malnourished individuals (Lochmiller et al. 1983,
Sams et al. 1996, Saino et al. 1997, Ditchkoff et al. 2001). Other factors of mortality
including accidents, drowning, and poaching have been reported (Cook et al. 1971,
Bartush and Lewis 1981, Huegel et al. 1985, Decker et al. 1992, Long et al. 1998, Ricca
et al. 2002, Pusateri Burroughs et al. 2006, Saalfeld and Ditchkoff 2007).
DOE SURVIVAL
Overall survival
Adult survival is less variable than juvenile survival and has a greater impact on
rate of population growth (Gaillard et al. 1998). Mortality in adults is likely to occur due
to the same major factors as juveniles, but the relative importance of each mortality factor
normally differs between fawns and adults. Populations in rural, exurban, and suburban
areas have different rates of mortality due to differences in hunting pressure, road
density, and predator ecology in these areas. Average annual mortality rates for adult
does in suburban areas and other areas which are not hunted can be as low as 18% (Etter
et al. 2002), but have been reported to be as high as 27% (Ricca et al. 2002) and 32%
(Hansen and Beringer 2003). While average annual mortality in an exurban area where
hunting was allowed, but not common, was 13% (Storm et al. 2007). Rural areas have
slightly greater or equal adult doe mortality rates in comparison to suburban and exurban
areas (21%, Nelson and Mech 1986; 31%, Fuller 1990; 29%, Nixon et al. 1991; 43%,
DePerno et al. 2000; 32 – 39%, Hansen and Beringer 2003; 24%, Brinkman et al. 2004),
however, low hunting effort can lead to very low mortality rates (13%, Campbell et al.
2005).
7
Hunting mortality
In most rural areas hunting is the most important source of mortality in female
white-tailed deer. Mortality due to both legal and illegal firearm hunting attributed to up
to 77% of deaths in a study by Hansen and Beringer (2003) and 43% of deaths in a study
by Brinkman et al. (2004). Mortality from illegal hunting has been reported in suburban
areas as 10% of overall mortality (Ricca et al. 2002). Hunting is normally allowed in
exurban areas but only with the permission of the landowner. Therefore hunting pressure
in these areas is dependent upon the landowners’ view of hunting. Storm et al. (2007)
found both legal and illegal hunting attributed to only 11% of mortality in an exurban
area where less than 20% of landowners allowed hunting.
Disease
Death due to disease is more frequent in suburban populations in comparison to
rural areas. One suburban study found that 28% of mortalities were due to disease in
combination with emaciation (Ricca et al. 2002). Diseases and parasites recorded
included pneumonia, Yersinia pseudotuberculosi, and lungworms. Of the 12 necropsied
deer, 92% lacked subcutaneous body fat and had high levels of ectoparasites (Ricca et al.
2002). As previously stated, increased disease in malnourished individuals is likely due
to affects of malnutrition on the immune system. In rural areas, disease caused 7%
(Brinkman et al. 2004) and 14% (Hansen and Beringer 2003) of adult doe mortality.
Hemorrhagic disease is the collective name for both epizootic hemorrhagic
disease and bluetongue, two viruses with identical clinical symptoms. Hemorrhagic
disease is found yearly in the Southeast (Nettles et al. 1992) and has the highest
prevalence in the coastal plain region (Stallknecht et al. 1991). Alabama and other states
8
in the southeast are considered to be endemic areas for hemorrhagic disease (Nettles et al.
1992). Deer mortality rates due to hemorrhagic disease are variable (Prestwood et al.
1974), but one study reports that mortality in endemic states is 16% (Nettles et al. 1992).
Usually hemorrhagic disease outbreaks do not limit population growth of white-tailed
deer (Howerth et al. 2008).
Vehicle collisions
Vehicle collisions are likely to cause greater mortality in areas with greater road
densities (i.e. suburban areas) in comparison to areas with decreased road densities (i.e.
exurban and rural areas). Death due to vehicle collisions was the greatest source of
mortality in two suburban studies, accounting for 90% (Hansen and Beringer 2003) and
42% of all mortalities (Etter et al. 2002). In another study, vehicle collisions were
reported as the second most frequent cause of death, after disease, and caused 17% of
mortalities (Ricca et al. 2002). Reports of doe mortality due to vehicle collisions in
exurban and rural areas have been as low as 2% (Storm et al. 2007) and 8% (Hansen and
Beringer 2003), however other studies show mortality rates in rural areas due to vehicle
collisions may be as great as 21% (Brinkman et al. 2004).
Predation
Predation in adult white-tailed deer is less likely than in fawns, however it does
occur, and varies based on the predator ecology of the area. Coyote predation on adult
white-tailed deer is thought to be minimal, and usually occurs when coyotes form packs
and/or when deer are injured (Andelt 1985, Campbell et al. 2005). Additional predators
of adult white-tailed deer include black bear (Campbell et al. 2005), wolf (17% annual
mortality, Nelson and Mech 1986), and unidentified felid (Brinkman et al. 2004).
9
COYOTE FOOD HABITS
As a generalist omnivore, coyotes opportunistically prey upon small and large
prey and eat fruit based on availability. The availability of these items varies based on
geographic location, creating changes in the coyote diet based on its home range. In its
native range in the central United States, a large diversity of small mammals, as well as
other prey, is available to the coyote. This greater diversity of small mammals creates
more opportunities for the generalist coyote. A food habits study in Oklahoma (Litvaitis
and Shaw 1980), indicated that over 50% of scats collected in winter contained small
rodents, including eastern woodrats (Neotoma floridana), white-footed mice (Peromyscus
leucopus), and cotton rats (Sigmodon hispidus). Deer fawns may also provide an
important seasonal food source in this area during fawning (Litvaitis and Shaw 1980,
Andelt et al. 1987). Lagomorphs are utilized in the West, primarily in fall, winter, and
spring (Litvaitis and Shaw 1980, Andelt et al. 1987). A decrease in lagomorphs in coyote
diet in summer may be due to cost tradeoffs between capturing and handling lagomorphs
in comparison to fawns and fruits (Andelt et al. 1987). After mammals, fruits and seeds
are the most important energy source based on consumption, occurring mostly in summer
and fall (Litvaitis and Shaw 1980, Andelt et al. 1987). Insects, mainly orthopterans, are
another important aspect of the coyote diet during summer, being found in 26% of coyote
scats (Litvaitis and Shaw 1980). The increase in fruits and insects in the coyote diet in
summer decreases the importance, and therefore may decrease hunting pressure, of
mammalian species (Connolly 1978, Andelt et al. 1987).
In comparison to the coyotes’ native range in the West, the Southeast offers a
decreased diversity of small mammals and a greater diversity of fruit. The low diversity
10
of small mammals leads to increased pressure on larger, more available prey items.
Percentage of occurrence of white-tailed deer in coyote scat is similar in winter and
spring months (40% Dec., 37% Mar., Schrecengost et al. 2008) and lower during the rest
of the year. White-tailed deer fawns occurred in over 30% of scats collected in May and
over 15% of those collected in June (Schrecengost et al. 2008). This occurrence
coincides with fawning (29% deer fawn occurrence in scats collected during fawning,
Stratman and Pelton 1997), a time when fawns are most vulnerable. Andelt et al. (1987)
found increased predation on deer by coyotes when fawning was delayed to a time when
fruit resources were less abundant. Unlike other white-tailed deer populations, fawning
in Alabama occurs primarily in August. If fawning in Alabama is occurring when fruits
are less abundant, increased predation may be seen on fawns. Other prey or carrion
species found in coyote scat include wild hog (Sus scrofa), lagomorphs, rodents, beetles,
and turkey-like egg shells (Wagner and Hill 1994, Stratman and Pelton 1997,
Schrecengost et al. 2008). Studies of coyote digestive tracts from Arkansas and
Tennessee have presented results that differ from other regional studies in that the diets of
coyotes had greater occurrences of rodents and lower occurrences of deer (Gipson 1974,
Smith and Kennedy 1983, Lee and Kennedy 1986). It should also be noted that these
three studies were conducted over 25 years ago, while the other studies presented here
were conducted in the last 15 years. This difference in time may account for the
differences in results due to changes in coyote or prey population densities and/or habitat
availability.
A greater diversity of fruit is found in the diet of coyotes in the Southeast. The
summer and fall diets of coyotes in the Southeast are predominately composed of fruits
11
and other vegetation, with prevalence rates as great as 85% (Stratman and Pelton 1997,
Schrecengost et al. 2008). Fruits included in the diet are wild plums (Prusus spp.),
smilax berries (Smilax spp.), blueberries (Vaccinium spp.), saw palmetto fruit (Serenoa
repens), blackberries (Rubus spp.), black cherry (Prunus serotina), poke berry
(Phytolacca americana), and persimmon (Diospyros virginiana; Stratman and Pelton
1997, Schrecengost et al. 2008). The diversity of southeastern fruiting plants allows the
coyote to use this as a major source of energy when available.
The studies discussed here primarily use the occurrence of a diet item in the
digestive tract or scat to determine its prevalence in the diet. However, differences in the
passage rate of diet items may skew these results. For example if one white-tailed deer is
depredated, hair is likely to be found in multiple scats from multiple individual predators
or scavengers whereas the remains of one persimmon will only be found in one scat. The
results of these studies can be used to compare items in the coyote diet and perhaps even
quantities of the same diet item, as the passage rate should be similar across individuals
within the same species. Scat studies cannot be used, however, to determine the extent of
white-tailed fawn predation, but using them to determine the timing of fawn predation is
possible (Salwasser et al. 1978, Litvaitis and Shaw 1980).
BOBCAT FOOD HABITS
Unlike generalist coyotes, bobcats tend to be specialized with a strictly
carnivorous diet (Litvaitis and Harrison 1989). The predominate prey species of the
bobcat are lagomorphs and small mammals (Davis 1955, Dibello et al. 1990, Godbois et
al. 2003). The incidence of bobcat predation on white-tailed deer is thought to be low in
12
most areas, and the predominance of white-tailed deer found in bobcat scats and
stomachs occurs during winter (Davis 1955, Young 1958, Maehr and Brady 1986,
Dibello et al. 1990). Bobcat food habit studies have demonstrated white-tailed deer
fawns in the diet during fawning season, but occurrences are very low (1 of 23 stomachs
during fawning period, Davis 1955; fawns occurred in 4% of scats during summer,
Dibello et al. 1990). In contrast to the low incidences of white-tailed deer reported in the
bobcat diet by most studies, Baker et al. (2001) found white-tailed deer hair in over 50%
of the scats collected in spring and summer from bobcats released on a coastal island of
Georgia. Wild hogs are another game species rarely found in the bobcat diet (Maehr and
Brady 1986).
PREDATOR-PREY THEORY
Basic concepts
Predation can either be compensatory or additive. Compensatory predation is
when predators remove no more individuals from a population than would have died
from other causes. Therefore compensatory predation does not affect population growth.
Additive predation is when predation adds to other sources of mortality. Therefore this
type of predation does affect population growth and can limit or regulate a prey
population. Removal studies indicating that removal of predators increases the prey
population are initial indicators that predators are having an additive affect on the prey
population.
Predation also relates to the numerical and functional response of the predator,
which together are considered the total response (Solomon 1949). The numerical
13
response is the change in the size of a predator population in response to changes in prey
density (there is typically a delay in this response), while the functional response is based
on consumption by an individual predator (there is no or little delay in this response;
Solomon 1949, Holling 1959). There are three types of functional responses (Holling
1959). The Type I functional response is based on a random search technique: as prey
density increases prey killed per predator increases proportionally until no more prey can
be consumed due to limitations caused by handling time (Holling 1959). This response is
most common in filter and passive feeders. In the Type II response, utilization increases
at a continually decreasing rate until an asymptote is reached based on searching and
handling time (Holling 1959, Evans 2004). The last functional response is Type III,
represented as an S-shaped curve. At low prey densities, utilization by the predator is
directly density-dependent, while at high prey densities it is inversely density-dependent
(Holling 1959, Evans 2004).
Limitation
Limitation is any process which reduces population growth, and includes all types
of mortality and reproductive losses (Messier and Crête 1985, Sinclair and Pech 1996). If
predators are limiting a prey population they are following a nonequilibrium model, and
the prey population will not recover to a previous level after being disturbed (Van
Ballenberghe 1987, Boutin 1992). The limitation theory is thought to occur in
populations where predator and prey have been heavily manipulated and there is little, if
any, density-dependent feedback between the populations (Gasaway et al. 1983). In the
limitation hypothesis predators could be following Type I or Type II functional responses
or have no functional response.
14
To determine which predator-prey theory is applicable to a population, Boutin
(1992) described three experiments and the results which would identify each theory.
While these experiments were originally created for moose (Alces alces) and wolves,
they are applicable to other predator-prey systems. In the first experiment, the density
and survival of the primary prey population are monitored in relation to changing
predator densities and densities of alternate prey for those predators (Boutin 1992). To
indicate the limitation theory, the results of this experiment would be as follows; at all
prey densities the predator will take an inversely density-dependent proportion of the
prey population, resulting in fluctuating prey densities (Boutin 1992). The second
experiment would reduce the prey population to a low density and require a non-
manipulated predator population (Boutin 1992). In this experiment a greater proportion
of the prey population is taken by predators, and a continued decrease in prey density
would be indicative of limitation theory (Boutin 1992). Extensive predator removal of all
predatory species until prey densities are able to increase to high levels is entailed in the
third experiment (Boutin 1992). This experiment will result in an increase in the density
of the prey population until predators return to the system and prey populations again
decline (Boutin 1992). This situation is indicative of limitation theory.
Banks et al. (2000) predicted that (1) red foxes limited juvenile recruitment of
kangaroos (Macropus giganteus) and removal of foxes would result in increased juvenile
survival, and that (2) population growth was limited by juvenile survival, so removal of
foxes would result in increased population growth. They found significantly greater
recruitment of juvenile kangaroos in fox removal sites versus non-removal sites,
supporting their first prediction (Banks et al. 2000). Greater densities of kangaroos at
15
removal sites versus non-removal sites seemingly supports the second prediction;
however, the investigators acknowledged the possibility that reduced predation pressure
may have resulted in increased observability of kangaroos, and initial high kangaroo
densities at all sites may have limited effects of fox removal (Banks et al. 2000).
Bergerud (1971) used historical data (1900 – 1967) from caribou (Rangifer
tarandus) in Newfoundland to report on factors affecting growth of the population. This
extensive amount of data allowed Bergerud (1971) to evaluate factors that were having
legitimate and lasting effects on the population. Poor recruitment and high hunting
pressure reduced the caribou herds from 1915 to 1930, and reduced hunting pressure, as
well as the loss of a large predator (Newfoundland wolf; Canis lupus beothucus) did not
result in a rebound of the population (Bergerud 1971). Bergerud (1971) concluded that
lynx (Lynx lynx) were limiting the caribou population through high predation on
juveniles, thus affecting population recruitment.
Regulation
Regulation occurs when a process is density dependent. The regulation predator-
prey theory is based on the interaction of density dependent factors with depressive
processes (i.e. predation, disease, starvation; Holling 1959, Messier and Crête 1985).
Prey populations following the regulation predator-prey theory will return to a previous
equilibrium level after perturbation (Boutin 1992, Sinclair and Pech 1996). Regulation
theory is based primarily on the total response; however, it requires a Type III functional
response.
There are two regulation theories based on the number of stable equilibrium.
With the single equilibrium model the total response curve intersects the population
16
growth curve at one point creating a single equilibrium. If the prey population is
disturbed the population will always return to this equilibrium (Boutin 1992). The
equilibrium will be influenced by changes in the population growth curve and the total
response curve (i.e. changes in predator density). The second regulation theory is known
as the multi-equilibria or predator-pit theory (Haber 1977). According to this theory, the
total response curve passes through the population growth curve three times, creating two
stable and one unstable equilibrium. This allows the predator to keep the prey population
at a low stable equilibrium, also known as a predator pit (Boutin 1992, Evans 2004). To
escape the predator pit the prey population must increase past a threshold, the unstable
equilibrium, to reach the upper equilibrium (Boutin 1992).
Boutin (1992) also gave predictions for regulation theories for his experiments. If
following a Type III functional response, the first experiment will result in density-
dependent proportions of the prey population being taken by predators at low to
intermediate prey densities and inversely density-dependent proportions at high densities
(Boutin 1992). However, the two regulation theories will differ in showing either one
stable equilibrium, or two stable equilibria. The second experiment will result in a
decrease in the proportion of prey taken by the predator population; however, prey
density will increase with one stable equilibrium and decrease with the two stable
equilibria theory (Boutin 1992). Similar to the predictions of experiment three for the
limitation theory, under one stable equilibrium regulation theory the prey population will
increase after predator removal and then decrease after predators move back into the
system. While with two stable equilibrium regulation theory predicts that the prey
population does not decrease after the reintroduction of the predatory species because the
17
prey population has reached an upper equilibrium (Boutin 1992).
Type III regulation, either one or two equilibria, is likely to be caused, in at least
the preferred species, when alternate species are present and also when the preferred
species has a refuge (i.e. an age class that is safe from predation; Murdoch and Oaten
1975). Generalist predators with a social organization that drives selected prey are more
likely to switch to alternate prey (Murdoch and Oaten 1975).
The lower equilibrium of the multi-equilibria theory is often referred to as the
predator pit. Predator populations are able to keep prey regulated at this low density,
with no affect to their own population density. Predator population density is maintained
by switching to alternate prey when the primary prey is at a low density (Shelton and
Healey 1999). Predators will still take available primary prey, while using alternate prey
to help maintain energy requirements, keeping the primary prey at the low equilibrium.
Generalist predators feed on a variety of animal and plant matter, and choose prey items
with the greatest benefit:cost ratio. This large prey base may interfere with predator prey
relationships and provide potential weakened regulatory mechanisms (Messier and Crête
1985). However it has also been suggested that switching to alternate prey may cause
greater stability in a system (Murdoch and Oaten 1975).
Messier and Crête (1985) were able to show that wolves regulate moose
populations in southwestern Quebec. Predation rates of wolves on moose were density
dependent at low and medium moose densities and inversely density dependent at high
moose densities (Messier and Crête 1985). Reduction in the wolf population allowed for
increased calf survival; however, the study did not continue to monitor for wolf recovery
and its effect on moose populations (Messier and Crête 1985), and therefore was not able
18
to determine if regulation allowed for one or two equilibria.
Using data from studies of moose and wolf interactions across the moose range,
Messier (1994) determined that wolves regulate moose at a high equilibrium, in a single
predator system, following what he termed the “Predation-food model (one-state)”. The
“predation-food model (one-state)” is a basic regulation model where predators interact
with prey populations at a high food-based equilibrium. With population growth reduced
by only 5-10%, however, wolves maintain the moose population at a low equilibrium,
Messier’s “predation model” (1994). A reduction in population growth is possible in
populations where alternate predators [i.e. black or grizzly bear (Ursus arctos)] affect
juvenile recruitment (Messier 1994). According to Messier (1994) the relationship curve
for wolves and moose does not allow for a predator-pit to develop.
Haber (1977) modeled moose, caribou, and wolf populations and discovered that
wolves were able to regulate prey populations at a low equilibrium when other
disturbance (i.e. hunting, winter mortality) reduced the prey population density. If wolf
populations were reduced in the model, prey populations were able to escape to a greater
density (Haber 1977). Haber (1977) was able to model and describe what is now termed
the predator-pit model or multi-equilibria model.
A study in Australia was able to show regulation of European rabbits
(Oryctolagus cuniculus) by red foxes (Banks 2000). Four study sites were selected and
foxes were removed from two sites. The removal sites had increased rabbit production
and populations were released from fox predation for 20 months (Banks 2000). While
both sites increased in rabbit density, one site had 20.3 times as many rabbits as pre-
removal densities while the other had 10.3 times as many rabbits (Banks 2000). Foxes
19
were able to reestablish populations at the sites after 20 months of removal and were able
to reach densities similar to non-removal sites in less than two years. After fox
reintroduction, rabbit densities initially dropped at both removal sites. Following the
breeding season, the rabbit population which had the greater density began to increase,
while the lower density population slowly declined (Banks 2000). This experiment
supports the predator-pit or multi-equilibrium theory, by showing that prey, rabbits, are
able to escape regulation by predators, foxes (Banks 2000).
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30
Chapter II: Survival Estimates of White-tailed Deer Fawns at Fort Rucker,
Alabama
ABSTRACT
Decreases in white-tailed deer (Odocoileus virginianus) fawn recruitment have
been noted at several locations across the Southeast. Understanding the reason for these
decreases is important for management of deer populations. We monitored fawns from
birth until 6 months to examine age- and cause-specific mortality rates, at Fort Rucker,
Alabama, a location that has experienced substantial decreases in fawn recruitment, deer
population density, and hunter success. During 2009 and 2010, 14 fawns were captured
immediately after birth and monitored: below average deer density resulted in low sample
sizes during the study. Of the 14 fawns captured, 3 survived until 6 months of age. Six
of 7 predation events were attributed to coyotes (Canis latrans) based on examination of
bite patterns and remains left at the site. We determined coyote density in the study area
during 2010 using DNA isolated from 44 opportunistically collected coyote scats. The
median rarefaction curve estimated density of coyotes at 0.40 coyotes/km2, with a 95%
confidence limit of 0.32 to 0.58 coyotes/km2. This study, like other recent studies in the
Southeast, has found that low fawn recruitment seems to be driven by greater levels of
coyote predation than originally believed. Coyotes are a recent addition to the predator
community of the Southeast, but how their addition will ultimately affect deer
populations remains ambiguous. Predator-prey theory predicts a variety of future
scenarios concerning predation rates, deer density, and responses to alternative
31
management strategies. We describe these alternative theories in regard to the current
state of knowledge.
INTRODUCTION
Recent changes in the predator community in the Southeast may be affecting
juvenile survival and population growth in white-tailed deer (Odocoileus virginianus).
Historically the terrestrial predator community of the Southeast consisted of red wolves
(Canis rufus), pumas (Puma concolor), bobcats (Lynx rufus), black bears (Ursus
americanus), and the smaller red (Vulpes vulpes) and grey foxes (Urocyon
cinereoargenteus). Within the last 40 years, coyotes (Canis latrans) have expanded their
range east into areas previously occupied by larger predators (Hill et al. 1987). Since the
recent increase in the coyote population, fawn recruitment in some populations in the
Southeast is thought to have decreased. Evidence of this has been documented in a recent
study in west-central South Carolina (Kilgo et al. 2010) in which fawn mortality was
estimated at 77%, with 89% of mortalities attributed to predation (Kilgo unpublished
data). Of all mortalities, 80% were confirmed or probable coyote predation (Kilgo
unpublished data). The effect of predation on fawn recruitment can also be seen in
studies that have examined predator control programs. The removal of predators (e.g.
coyotes and bobcats) from study areas in southwest Georgia (Howze et al. 2009) and
northeast Alabama (VanGilder et al. 2009) have lead to increases in fawn recruitment. It
is important to note however, that in the absence of coyotes, bobcats may also cause
significant rates of mortality in white-tailed deer fawns. A study done on an island off
the coast of South Carolina found overall survival to 26 weeks to be 21.1%, with 67% of
32
mortality attributed to bobcats (Roberts 2007).
How this change in the predator community will ultimately affect deer
populations in the Southeast is unknown. It is possible to refer to areas where coyotes are
non-native but have been established for longer periods (e.g. the Northeast) to understand
how this new predator may affect the Southeast. While differences in climate and habitat
may cause differences in survival between these areas, previous research in the Northeast
provides a foundation of knowledge that can be used to predict impacts on deer
populations in the Southeast. A review of research in the Northeast indicates that areas
with no coyotes or other large predators have comparable or greater 6-month fawn
survival (70%, Banasiak 1961) to areas with coyotes (76%, Decker et al. 1992; 65%,
Long et al. 1998; 66%, Ballard et al. 1999; 45.6-58.6%, Vreeland et al. 2004). Annual
survival of fawns in Northeast populations, where monitoring occurred for an entire year,
was much lower than 6 month survival (26%, Long et al. 1998; 25%, Ballard et al. 1999).
The large difference between 6 month and annual survival indicates that winter survival
is an important aspect of juvenile survival in the Northeast. Differences in deer density
and coyote density are likely to account for some differences in survival rates among
these studies, but differences in climate, habitat, and available alternative prey may cause
greater differences between these studies and those in the Southeast.
Recent studies in the Southeast indicate that coyotes are affecting juvenile
survival (Saalfeld and Ditchkoff 2007, Howze et al. 2009, VanGilder et al. 2009, Kilgo et
al. 2010), and while comparisons to the Northeast provide a foundation they will not be
completely applicable due to differences in climate and habitat. This leaves managers in
the Southeast ill-equipped to resolve this new management issue. To understand the
33
effects of this new predator in the Southeast it is necessary to understand how changes in
densities of coyotes and deer affect juvenile survival and population growth. These
parameters can be used in conjunction with predator-prey theory to correctly address this
new problem. Understanding which predator-prey theory is most applicable in the
Southeast will allow managers to implement effective and economically efficient
management strategies.
Deer ecology in Alabama and other Gulf Coast deer populations differs in
comparison to the Southeast and all other regions, due to differences in timing of the
breeding season. Breeding in Alabama occurs primarily in January and fawns are born
during August (Lueth 1955, 1967, Gray et al. 2002), while across most of North America
deer breed in November and fawn during May and June (Verme and Ullrey 1984).
Delayed fawning in Alabama affects alternate food availability and coincides with coyote
pup independence (Harrison and Harrison 1984, Harrison et al. 1991). These factors may
cause differences in fawn survival in Alabama in comparison with other populations and
may result in a unique management situation.
To begin to understand how differences in coyote and deer population densities
affect juvenile survival and population growth we estimated juvenile and adult doe
survival, as well as coyote density at Fort Rucker, Alabama, where recent increases in
coyote numbers and decreases in white-tailed deer density have been noted. Conducting
this study in Alabama also gave us the opportunity to assess the effect of a late fawning
season on juvenile survival (see Saalfeld and Ditchkoff 2007).
34
STUDY AREA
This study was conducted at Fort Rucker, Alabama, a 183-km2 military facility
that conducts helicopter training for the U.S. Army. The southeastern third of the facility,
approximately 42 km2, was chosen as the area most feasible for this study. After data
collection, unsampled sections of the study area were disregarded and the adjusted study
area consisted of 31.6 km2. The vegetation on the area was mostly of forested land that
was comprised of primarily pine (Pinus spp.) and mixed pine-hardwood forests.
Dominant tree species included loblolly (Pinus taeda), shortleaf pine (Pinus enchinata),
longleaf pine (Pinus palustris), slash pine (Pinus elliottii), southern red oak (Quercus
falcate), water oak (Quercus nigra), laurel oak (Quercus laurifolia), sweetgum
(Liquidambar styraciflua), yellow-poplar (Liriodendron tulipifera), sassafras (Sassafras
albidum), dogwood (Cornus spp.), sourwood (Oxydendrum arboretum), hawthorn
(Crataegus spp.), persimmon (Diospryros virginiana), and cherry (Prunus spp.). The
study area had patches of sandhill forest which included turkey oak (Quercus laevis),
bluejack oak (Quercus incana) and were dominated by longleaf pine (Mount and
Diamond 1992). Plantations of loblolly pine averaging 25 years of age, slash pine
averaging 30 years of age, and longleaf pine averaging 15 years of age were dispersed
throughout the area. Additionally, wildlife food plots planted with a variety of wildlife
crops and fruiting trees were dispersed throughout the area. Prescribed burning had
occurred throughout the area since before 1980, but had recently increased in frequency
and intensity.
Both firearm and archery hunting were allowed on the majority of Fort Rucker.
In recent years Fort Rucker hunters have reported a total harvest of 30 to 100 deer for the
35
entire installation. The majority of Fort Rucker had a 2.4-m chain linked fence with
barbed wire at the top; however, there were breaks over streams and for natural
boundaries. This fence limited, but did not prohibit, movement of individuals to and
from the population.
Camera studies on Fort Rucker have shown fawn recruitment to be as low as 0.20
fawns per doe in the last five years. Populations without coyotes in the Southeast have
reported fawn recruitment estimates as high as 0.80 (Kilgo et al. 2010). Five years of
data collection have shown that pregnancy rates at Fort Rucker were above 90% (Cook
unpublished data), suggesting that depressed recruitment rates were not a function of low
rates of pregnancy. Additionally, low recruitment was not believed to be due to changes
in cover, habitat type, or yearly climate, as body weights and herd health checks had
indicated that the population was in excellent physical condition.
METHODS
Doe capture and handling
From February to July of 2009 and 2010, we trapped does using cannon nets over
areas baited with corn (Hawkins et al. 1968): trap sites were baited for at least a month
before capture started (Ditchkoff et al. 2001). After capture, does were sedated using a
combination of 125 mg of telazol to 100 mg of xylazine (1 ml/45.36 kg) injected
intramuscularly. To reverse sedation, an intramuscular injection of tolazine (yohimbine
hydrochloride; 3 ml/45.36 kg) was given after data collection and vaginal transmitter
insertion (Saalfeld and Ditchkoff 2007). We determined age by tooth wear and
replacement (Severinghaus 1949), and measured chest girth, and head, body, tail, and
36
right hind foot length using a flexible 2-m tape. While the deer were sedated, we inserted
vaginal implant transmitters (VITs; M3960B, Advanced Telemetry Systems, Insanti,
MN) approximately 20 cm into the vaginal canal with the silicone wings pressed against
the cervix (Carstensen et al. 2003, Saalfeld and Ditchkoff 2007). These VITs were
capable of sensing a temperature change of 34 to 30 C, and would change the pulse
frequency signal emitted when expelled from the doe during parturition. All does were
also fitted with VHF radio collars (M2510B, Advanced Telemetry Systems, Insanti, MN)
to allow regular monitoring of survival and location before and after fawning. We
monitored does approximately once a week from initial capture until more intense
monitoring began in mid-July, approximately two weeks before the peak of birth in
Alabama (Lueth 1955, 1967). After giving birth, does were located when their
corresponding fawn(s) were located.
Fawn capture and handling
Vaginal transmitters were monitored three times a day beginning in mid-July.
After the first birth of the season, we monitored transmitters every six hours. Monitoring
continued until all vaginal transmitters were expelled or the doe was identified as
nonpregnant. We determined if a doe was nonpregnant by examining photographs, taken
by remote cameras over baited sites, for visible signs of pregnancy. Fawns were not
approached until at least two hours after the VIT indicated expulsion. A precise event
timer in the vaginal transmitter allowed for time of birth to be calculated to within 30
minutes.
We followed the methods of Roberts (2007) and Kilgo (unpublished data) to
locate fawns that moved from the birth site. Prior to approaching the VIT, the dam was
37
located via VHF telemetry and approached on foot. This increased the possibility of
finding fawns if they had moved from the birth site and followed the doe. If the fawn(s)
were not found near the doe, we moved towards the expelled VIT. If we did not find the
fawn before or when the birth site was reached, we continued to search in expanded
circles around the birth site. A thermal imaging camera (Raytheon Palm IR 250D,
Waltham, MA) was used to aid in conducting all searches.
We captured fawns by hand and used non-scented latex gloves to reduce scent
transfer (Powell et al. 2005, Saalfeld and Ditchkoff 2007, White et al. 1972). Fawns were
weighed, sexed, and fitted with expandable collars (M4200, Advanced Telemetry
Systems, Insanti, MN) that were designed to fall off at approximately 6 months of age.
Using a flexible 2-m tape, we measured full body length, right hind foot length, and new
hoof growth. Handling was completed in an efficient manner to reduce stress, and
handling times were normally less than 10 minutes per fawn.
In addition to using VITs to find fawns at the birth site, we also conducted
additional searches to increase the number of fawns captured. These searches were
conducted using a window-mounted thermal imager and driving slowly along dirt roads
through the study area as described by Ditchkoff et al. (2005). To increase the
probability of catching older, more agile individuals, fawns were approached slowly in a
sporadic manner with a handheld net (Ditchkoff et al. 2005). We aged fawns that were
captured with this technique using hoof growth measurements (Sams et al. 1996), and
handled these fawns in the same manner as those captured with the aid of VITs.
Fawns were located at least once every day for the first 2 months, and then
located once a week until they reached six months of age or the expandable collar fell off.
38
When we received a mortality signal, the fawn was immediately located and the cause of
death determined. Cause of death due to predation was determined by assessing remains
at the site for puncture wounds and evidence of predators such as hair, scat, or tracks
(O’Gara 1978). All other causes of death were determined during necropsy by the State
of Alabama Department of Agriculture, Thompson Bishop Sparks Diagnostic Lab,
Auburn, AL.
Analysis
All analysis was conducted in Program R version 2.10.1 (The R Foundation for
Statistical Computing, 2009). Age-specific survival rate of fawns was estimated until
180 days using a Kaplan-Meier survival curve without staggered entry, and any
individuals with an unknown fate were right censored (Hosmer et al. 2008). To compare
hazards of covariates, including sex, year, age, and age2, we used a Cox proportional
hazards model (Hosmer et al. 2008). In this model, entries were staggered based on date
of birth (i.e. July 27) to allow the effects of age to be tested. Cause-specific mortality of
fawns was analyzed using competing risks analysis; three types of mortality were used in
this analysis, abandonment, bobcat predation, and coyote predation (Heisey and Patterson
2006). Annual doe survival was estimated using a Kaplan-Meier survival curve with
staggered entry at weekly increments with week 1 starting on January 1st. Individuals
surviving into the next calendar year were right censored and reentered in the data as a
separate entry (recurrent model; Fieberg and DelGiudice 2009). We used Cox
proportional hazards models to compare hazards of covariates including age and year
(Hosmer et al. 2008).
39
Coyote density
Coyote density was estimated for summer 2010 by identifying individual coyotes
within the study area using DNA extracted from scat. Scat samples were collected
opportunistically on roads throughout the study area from June to September 2010.
Samples were taken along the side of the fecal sample, and 0.4 mL of feces was placed
into vials containing 1.5 mL DETs buffer (Stenglein et al. 2010). Genetic analyses were
conducted by the Laboratory for Conservation and Ecological Genetics, University of
Idaho using techniques described by Stenglein et al. (2010).
We iterated a rarefaction curve, an accumulation of unique individuals or
genotype with the asymptote representing the estimated population size [y = (a*x)/(b +
x), where x was the number of amplified samples, y was the cumulative number of
unique genotypes, a was the asymptote, and b the rate of decline in the slope], 1,000
times to determine the number of coyotes in the study area (Kohn et al. 1999). The
median, rather than the mean (Frantz and Roper 2006), number of coyotes, as determined
by the rarefaction curves, was used to determine coyote density on the adjusted study
area (31.6 km2).
RESULTS
We captured 15 does and recaptured one doe in year 2 of the study, resulting in 16
deployed VITs during 273 trap sessions over two field seasons: 9 VITs were deployed in
2009 and 7 in 2010. The 16 deployed VITs resulted in 11 birth events: 6 in 2009 and 5 in
2010. Twelve live fawns (4 in 2009 and 8 in 2010) and 2 stillborn fawns in 2009 were
found at or near VIT birth sites. In 2009, one VIT was expelled prematurely, although a
40
fawn was found within 24 hours of birth near the doe. One additional fawn was found
during random searches in 2009. Capture efforts resulted in a total of 15 does and 14
fawns for survival analysis.
Overall probability of fawn survival to six months of age was determined to be
0.26 (95% CI = 0.10 – 0.68) with 3 of 14 fawns surviving. All mortalities occurred
between 3 and 40 days of age, but no patterns of mortality were apparent within this
period (Figure 1). No covariates were found to be significant predictors of mortality,
based on a full model including age (P= 0.739, β = 0.972, 95% CI = 0.821 – 1.15), age2
(P = 0.878, β = 1.00, 95% CI = 0.999 – 1.001), sex (P = 0.606, β =0.571, 95% CI =
0.0680 – 4.80), and year (P = 0.762, β = 1.57, 95% CI = 0.0847 – 29.08).
Three types of mortality were identified: abandonment, bobcat predation, and
coyote predation. Vehicle collisions were not a cause of mortality for any individual
within the study; however, other fawns without radio collars were noted to have died
from vehicle collisions within the study area. Competing risks analysis determined that
the probability of mortality by 180 days of age due to abandonment, bobcat predation,
and coyote predation was 0.154 (95% CI = 0 - 0.329), 0.125 (95% CI = 0 - 0.327), and
0.649 (95% CI = 0.138 – 0.857), respectively. Since fawns were monitored daily during
the time frame when all mortalities occurred we are confident that scavenging events
were not misdiagnosed as predation.
Annual probability of doe survival was 0.75 (95% CI = 0.58 – 0.965) with 10 of
15 does surviving throughout the study period. No covariates were found to be
significant predictors of mortality, including age (P = 0.557, β = 1.411, 95% CI = 0.447 –
4.45) and year (P = 0.322, β = 0.383, 95% CI = 0.0574 – 2.56). Causes of morality
41
included poaching (n = 1), disease (hemorrhagic disease; n = 2), and unknown causes (n
= 2). Mortality was attributed to unknown causes due to extensive scavenging or
relocation of the collar by scavengers prior to discovery of the mortality event. Although
hunting was allowed on the study area, no hunting related mortalities could be
determined. Mortality occurred throughout the year with no apparent discernable
patterns (Figure 2).
Forty-four of 60 coyote scat samples sent for analysis were used to determine
coyote density within the adjusted study area. The 16 samples which were not used in
analysis were due to lack of amplification (n = 6), incorrect species (n = 2), inability to
determine individual (n = 5), or the scat was found outside of the adjusted study area (n =
3). Ten individuals were identified from these samples, and 1000 rarefaction curves of
bootstrapped sampling taken with replacement resulted in a median number of 12.78
(95% CL = 10.21 – 18.48) coyotes in the area. When density was adjusted for the size of
the study area (31.61 km2), coyote density was determined to be 0.40 (95% CL = 0.32 –
0.58) coyotes/km2.
DISCUSSION
Fawn survival to 180 days in our study was 0.26, and survival of adult does was
0.75; however, confidence intervals were large for both survival rate estimates due to low
sample size. We were unable to determine if any variables in our models affected
survival, but whether this was due to the variables not affecting survival or a product of
low sample size is unknown. Assuming our estimated rates of survival are representative
of the population at Fort Rucker, fawn survival was less than historic averages for white-
42
tailed deer (54%, Linnell et al. 1995) and consistent with more recent studies of fawn
survival in the Southeast (33.3%, Saalfeld and Ditchkoff 2007; 23%, Kilgo unpublished
data). The fawn survival estimate from this study was also consistent with recruitment
estimates (0.20 fawns per doe, Mayo personal communication) at Fort Rucker that were
obtained using game cameras to estimate fawn:doe ratios. Survival of adult does on Fort
Rucker was in the range of reported estimates of other rural white-tailed deer populations
(79%, Nelson and Mech 1986; 69%, Fuller 1990; 71%, Nixon et al. 1991; 57%, DePerno
et al. 2000; 61 - 68%, Hansen and Beringer 2003; 76%, Brinkman et al. 2004).
Our data suggest that low recruitment at Fort Rucker was the result of high rates
of predation on fawns which has been documented in other recent studies in the Southeast
(Saalfeld and Ditchkoff 2007, Howze et al. 2009, VanGilder et al. 2009, Kilgo
unpublished data). Coyotes were the leading cause of fawn mortality in our study and the
probability of mortality due to coyotes was estimated to be 0.649. Coyotes potentially
caused up to 63% of mortalities in white-tailed deer fawns in an Alabama population
(Saalfeld and Ditchkoff 2007) and 80% of mortalities in a South Carolina population
(Kilgo unpublished data). These recent studies differ from previous fawn survival studies
in the Southeast because coyotes are the leading predator, where prior studies found
bobcats to be the leading predator (Epstein et al. 1983).
Exactly how this low recruitment affects long-term densities of white-tailed deer
populations is somewhat unknown, but some researchers have begun to explore methods
that could minimize impacts of predation on recruitment rates. Howze et al. (2009)
removed 30 – 40% of the predator populations from January to August 2008 with most
removal occurring during the fawning season and VanGilder et al. (2009) removed close
43
to 100% of the predator populations before peak fawning in 2007. Both of these studies
reported that removal of bobcats and coyotes led to an increase in fawn recruitment.
These results suggest that predation in these systems was additive. In systems where
predation is compensatory, predator removal will theoretically have no affect on
population growth, while additive predation will result in increased population growth
following predator removal. Although these studies improved our understanding of the
potential impacts of predator control programs on fawn survival and subsequent
population growth, they were only conducted for one year each and did not evaluate the
long term effects of predator control or contribution of increased fawn recruitment to
growth of the deer population. To accurately predict the long term effects of
management prescriptions such as predator control programs, population parameters
(density, recruitment, etc.) must be regularly monitored. These data can then be used to
definitively determine if predators are having an additive or compensatory effect on
white-tailed deer populations.
Interactions of white-tailed deer and coyotes should follow basic principles of
predator-prey theory, and an understanding of these theories can be useful in predicting
impacts of management prescriptions (predator control programs, etc.). Mortality factors
can be described as either limiting or regulating to prey populations. Limitation is any
process that reduces population growth, and includes all types of mortality and
reproductive losses (Messier and Crête 1985, Sinclair and Pech 1996). In limiting cases,
predation may be described as either additive or compensatory. If a prey population is
limited by food resources, predation is most likely compensatory, and any losses
associated with predation do not measurably increase rates of mortality. If predation is
44
additive and predators are limiting a prey population, then the predator and prey
populations can be described as following a nonequillibrium model, and the prey
population will not recover to previous levels after being disturbed (Van Ballenberghe
1987, Boutin 1992) without a reduction in predation. Regulation occurs when a
depressive process (i.e. predation, disease) is density dependent (Holling 1959, Messier
and Crête 1985). Prey populations that are being regulated by predators will return to a
previous equilibrium level after perturbation (Boutin 1992, Sinclair and Pech 1996).
To distinguish between limiting and regulating predator-prey interactions, it is
essential that the functional (Type I, II, or III; Holling 1959) and numerical responses of
the predator be understood. For predation to be limiting, predators can be described as
following a Type I or Type II functional response, or have no functional response. The
Type I functional response is based on a random search technique: as prey density
increases, the number of prey killed per predator increases proportionally until no more
prey can be consumed due to limitations caused by handling time (Holling 1959). In a
Type II functional response, predation increases at a continually decreasing rate until an
asymptote is reached based on search and handling time (Holling 1959, Evans 2004). If
predation is density-dependent at low prey densities and inversely density-dependent at
high prey densities (Type III functional response; Holling 1959, Boutin 1992), then
predators are regulating the prey population (Pech et al. 1992).
According to regulating theory, the density at which a prey population reaches
equilibrium is dependent upon the interaction of the prey population growth curve in the
absence of predators, and the total response (e.g., the combination of the functional and
numerical responses; Solomon 1949) of the predator population. It is possible for either
45
one or two stable equilibria to develop depending on the relationship between these two
curves (see Appendix II). If two stable equilibria develop, the model or theory is
described as multi-stable state or a predator pit (Haber 1977). The predator pit refers to
the lower equilibrium (e.g., density) at which prey are regulated by predation. According
to theory, the prey population is unable to escape the predator pit and grow to greater
densities without a reduction in predation and/or other mortality factors. Until the prey
population increases above a third, non-stable equilibrium point (Boutin 1992, Evans
2004) that is located between the two stable equilibria, they will remain in the predator
pit. To understand which of these theories applies to white-tailed deer and coyotes in the
Southeast, a thorough understanding of the interaction between deer density and rates of
predation amidst stable coyote densities is required (Boutin 1992). Additionally, the
response of deer density and rates of predation must be understood in the context of
changing coyote densities (Boutin 1992).
Due to the ecology of coyote and deer interactions, it is most likely that coyotes
are regulating deer populations. When a predator switches between available prey
species, a Type III functional response can occur (Murdoch and Oaten 1975). Generalist
predators, such as coyotes, are more likely to switch prey than specialist predators and are
likely to switch as a group based on their social organization (Murdoch and Oaten 1975).
A Type III functional response can also arise when the prey species has an age class that
is safe from predation (i.e. refuge, Murdoch and Oaten 1975); the mature adult age class
in white-tailed deer is less likely to be depredated than the juvenile age class, as seen in
our study. These two ecological factors strongly suggest that a Type III functional
response is occurring in this system and coyotes are regulating deer.
46
We believe that deer-coyote interactions at our study site, and possibly in other
locations across the Southeast, follow the patterns predicted of a regulation model, and
the deer population at Fort Rucker is potentially in a predator pit. Prior to the mid to late
1980’s, Fort Rucker had an overpopulated deer herd that exhibited signs of poor
condition (e.g., low body weights, levels of parasitism; Mayo, personal communication).
At this time, a substantial increase in antlerless harvest was applied in a concerted effort
to reduce deer densities. In the greatest reported harvest year, 1987, over 600 deer were
harvested, and between 400 and 650 deer were harvested each year from 1984 to 1991.
Efforts to reduce the deer population were suspended in 1995 after harvest numbers and
apparent deer densities reached a point that was considered to be below what was
originally desired. Although the deer population was expected to rebound following
reductions in hunting pressure, the rebound never occurred, and the annual deer harvest
has been <200 deer for the last 15 years and <120 animals for the past 10 years.
All indications are that Fort Rucker currently has a deer population that is below
average for Alabama and the Southeast, and there has been no apparent increase in the
population despite substantial decreases in hunter harvest. However, without data on
coyote densities or the proportion of deer killed by coyotes before and after the deer
population decreased, it is not possible to determine with certainty if the deer population
at Fort Rucker is being regulated according to a single or multi-equilibria model. If the
coyote density is assumed to have been constant throughout the sequence of events at
Fort Rucker, the events suggest a predator pit: a high deer population was pushed below a
threshold due to intense hunting, and a lower density was established from which there
has been little or no increase despite decreased hunter harvest. If coyote density
47
increased during these events, regulation could be occurring at a single low equilibrium
as described by Messier’s (1994) predation model. It is impossible to state with total
confidence that this is in fact a predator pit or regulating scenario, as it is also possible,
although unlikely in our opinion, to conclude that predation is not density dependent and
coyotes are limiting the local deer population.
Fort Rucker is not the only property that has observed low recruitment and low
deer densities in the Southeast. Two other studies have recently reported low recruitment
with below average deer densities (4 - 8 deer/km2, Johns and Kilgo 2005; 3.8 - 5.8
deer/km2, Howze et al. 2009). A third study has also reported low recruitment of fawns
following heavy doe harvest, and attributed the low recruitment to predation (VanGilder
et al. 2009). Unfortunately, recent data on population growth, fawn survival, and
recruitment have not been reported for other Southeastern deer populations with average
or above average densities, thus preventing comparisons with these reported studies.
This is the first known study in the Southeast to report both survival rates of white-tailed
deer and estimated coyote densities. Presenting both of these estimates creates a baseline
for comparison with future studies and could help to elucidate our understanding of the
interactions between these two species.
Current views and popular practices of white-tailed deer and coyote management
in the Southeast could be influenced by a more complete understanding of the predator-
prey model that best describes this system. Quality deer management (QDM) is a
growing practice in the Southeast and promotes densities in balance with habitat
conditions and healthy buck:doe ratios (Miller and Marchinton 1995). In most cases, the
management prescription that is applied to achieve these objectives is increased antlerless
48
harvest. Populations maintained at a low equilibrium by coyotes through single state
regulation may decline even further with increased antlerless harvest because this harvest
will most likely be additive (Nelson and Mech 1981, Messier and Crête 1985). However,
if coyotes are regulating deer populations through multi-stable equilibria as we believe,
then antlerless harvest programs that do not account for deer density could theoretically
result in deer populations that are driven to a density from which they are unable to
recover without help. Because of this possible outcome, deer management programs that
promote high antlerless harvest should ensure efforts are in place to monitor vital deer
population parameters to guarantee that deer densities are driven to and maintained at
desired levels.
Coyote management in most states in the Southeast consists of hunting
regulations with no bag limit and an open season throughout the year. Whereas these
regulations are designed to allow managers to control coyote density, successful control
programs for coyotes are expensive (Pearson and Caroline 1981). An understanding of
predator-prey theory can improve the impact of predator management programs on deer
populations. Using theory as a guide, managers could estimate an optimal coyote density
and only remove necessary individuals, thereby reducing costs. If coyotes are regulating
or limiting deer populations, it is likely that coyote densities will need to be assessed
annually. However, in areas with multi-stable equilibria, it may be possible to manage
deer populations without predator control if deer populations are greater than the non-
stable equilibrium or threshold. After this point, assuming predator-prey dynamics do not
change, only an outside event that results in increased mortality (disease, hunting
pressure, etc.) should be able to drive deer densities below the non stable-equilibrium
49
point.
To determine the predator-prey model that defines deer-coyote dynamics in the
Southeast, additional research is needed. Efforts should focus on examining the
interaction of these species at varying deer and coyote densities. Determining which
theory applies in the Southeast will allow managers to estimate optimal deer densities and
rates of predation for specific areas. Non-hunted or minimally-hunted populations may
benefit from greater coyote densities than hunted properties, while managers of hunted
areas will likely need to incorporate deer harvest rates into the appropriate predator-prey
model to predict the combined effects of coyotes and hunters on the local deer
population.
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Figure 1. Survival of white-tailed deer fawns from birth to 180 days of age at Fort
Rucker, AL during 2009 and 2010.
58
Figure 2. Annual survival of female white-tailed deer at Fort Rucker, AL. These data
were determined based on survival from capture week in either 2009 or 2010 until the
end of the study in February 2011.
59
APPENDIX I
Table 1. Doe data
________________________________________________________________________
Doe ID Date of Transmitter Fate Fawns Caught Date of Cause of
Capture or Recovered Death Death
________________________________________________________________________
D1 4/16/2009 premature expulsion 1 ----------- Survived
D2 4/16/2009 successful 1 ----------- Survived
D2 3/15/2010 successful 2 ----------- Survived
D3 4/29/2009 successful 2 (stillborn) 11/13/2009 Unknown
D4 5/8/2009 ----------- 2 (fetal) 7/8/2009 Disease
D5 5/8/2009 successful 2 8/4/2010 Unknown
D6 5/20/2009 not pregnant 0 ----------- Survived
D7 5/20/2009 successful 1 ----------- Survived
D8 6/30/2009 not pregnant 0 2/13/2010 Poached
D9 7/13/2009 successful 0 ----------- Survived
D10 2/24/2010 successful 0 ----------- Survived
D11 4/27/2010 not pregnant 0 ----------- Survived
D13 6/1/2010 ----------- 2 (fetal) 6/30/2010 Disease
D14 6/1/2010 successful 2 ----------- Survived
D15 6/9/2010 successful 2 ----------- Survived
D16 7/8/2010 successful 2 ----------- Survived
________________________________________________________________________
60
Table 2. Fawn data
________________________________________________________________________
Fawn ID Doe ID Birth Date Sex Date of Death Age (Days) Cause of Death
________________________________________________________________________
F1 D1 7/27/2009 M 8/16/2009 20 Coyote Predation
F2 D5 7/31/2009 F 8/23/2009 23 Coyote Predation
F3 D5 7/31/2009 M 9/9/2009 40 Coyote Predation
F4 D2 8/1/2009 F ----------- Survived
F6 N/A 8/15/2009 F ----------- 1 Right Censored
F7 D7 8/20/2009 M 9/20/2009 31 Coyote Predation
F8 D15 8/3/2010 F 8/7/2010 4 Coyote Predation
F9 D15 8/3/2010 M ----------- Survived
F10 D2 8/4/2010 F 8/7/2010 3 Abandoned
F11 D2 8/4/2010 M 8/7/2010 3 Abandoned
F12 D16 8/11/2010 M 8/24/2010 13 Bobcat Predation
F13 D16 8/11/2010 F ----------- Survived
F14 D14 9/3/2010 M ----------- 11 Right Censored
F15 D14 9/3/2010 M 9/12/2010 9 Coyote Predation
D3 8/28/2009 M ----------- 0 Stillborn
D3 8/28/3009 F ----------- 0 Stillborn
________________________________________________________________________
61
APPENDIX II
Figure 1. The interaction between the population growth curve and the Type III
functional response resulting in two stable equilibria, A and C, and one unstable
equilibrium, B, also known as the multi-stable state or predator pit theory.
Population growth curve
Type III functional response
Iunctionalresporesponse
Prey density
Per
cen
t p
rey d
epre
date
d
Pop
ula
tion
gro
wth
62
Figure 2. The interaction between the population growth curve and the Type III
functional response resulting in one stable equilibrium, A, also known as the regulation
theory.
Population growth curve
Type III functional response
Iunctionalresporesponse
Per
cen
t p
rey d
epre
date
d
Pop
ula
tion
gro
wth
Prey density
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